Traditional univariate analysis of fMRI data identifies differences in the average activity of specific brain regions under different conditions. In contrast, Multi-Variate Pattern Analysis (MVPA) classifies patterns of fMRI activity under different conditions. Both methods infer neural activity based on a hemodynamic response following the onset of a stimulus. It is an open question whether peak classification accuracy using MVPA occurs at, before, or after the peak in the BOLD signal level. Because neuronal activity is fast, it is possible that pattern classification accuracy is high within hundreds of milliseconds, even when the BOLD signal level is low. In other words, even very low average levels of hemodynamic activity, such as that which occurs during the initial negative dip in BOLD signal level, might produce highly informative activation patterns classifiable using MVPA. Alternatively, it is possible that the peak in MVPA classification accuracy occurs at the same temporal lag as the peak in BOLD signal level. To assess these possibilities, we performed an fMRI experiment with a slow event-related design, using faces and houses as stimuli, and explored the activity within functionally defined regions of interest from striate cortex to object-selective temporal cortex. We compared the average hemodynamic response to the classification accuracy over time. Our results suggest that there is a correlation between BOLD signal level and classification accuracy such that the peak in classification accuracy occurs at approximately the same temporal lag from stimulus onset as the peak in the BOLD signal level following stimulus onset.